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Abstract:

Providing for characterizing and determining effectiveness of social
networks is described herein. By way of example, data descriptive of
inter-relationships of persons can be employed to generate a social
connectivity map for users of a communication network. Data disseminated
or consumed via the communication network can be monitored and
characterized in conjunction with task performance. The characterization
can be compared with a performance benchmark to rate a composition of a
social network, or underlying network applications and functions, in
effecting user tasks or other user activities. Accordingly, individuals
and organizations can determine and compare the effectiveness of a
network in assisting user activities based on predetermined benchmarks,
which can be tuned to various aspects, functions or applications of an
underlying social network.

Claims:

1. A performance characterization system for a communication network,
comprising:a memory that obtains and stores data descriptive of a subset
of inter-personal relationships of a set of users of a communication
network,a tracking component that processes data transmitted via the
communication network and monitors dissemination or consumption of the
data by user devices; anda rating component that compares the
dissemination or consumption against a performance benchmark to establish
a performance rating for an electronic representation of a social network
(electronic social network) based on the subset of inter-personal
relationships.

2. The system of claim 1, further comprising an analysis component that
characterizes the dissemination or consumption of the data by measuring a
rate of dissemination or consumption, a task completion rate or a degree
of user-interaction in conjunction with completing a task.

3. The system of claim 1, the tracking component analyzes e-mail, text
message, short message service (SMS), instant message (IM), website
forum, really simple syndication (RSS) or voice-to-text communication
implemented via a user device to measure the dissemination or consumption
of the data.

4. The system of claim 1, further comprising:a weighting component that
scores an impact of a network communication on the dissemination or
consumption of the data; andan aggregation component that compiles impact
scores to facilitate characterization of the dissemination or consumption
of the data.

5. The system of claim 4, the weighting component scores data relative to
a top-down or viral dissemination of the data within an organization of
users.

6. The system of claim 1, further comprising:a data store that contains a
plurality of performance benchmarks each pertaining to a different
network of users, a type of network or a function of a network; anda
selection component that imports a selected performance benchmark from
the data store to rate a type, aspect or function of the electronic
social network.

7. The system of claim 1, further comprising a data mining component that
obtains and analyzes Internet data stores, communication network data
stores, user device data stores or user input to obtain the subset of the
inter-personal relationships.

8. The system of claim 1, further comprising:an optimization component
that identifies changes to the communication network or composition of
the electronic social network to improve the performance rating relative
the performance benchmark; andan output component that exposes the
identified changes at one of the user devices.

9. A computer-implemented method for establishing performance of a
communication network, comprising:obtaining data descriptive of a subset
of inter-personal relationships of a set of users of a communication
network, the inter-personal relationships comprise a social
network;monitoring a user device providing an interface to the
communication network for dissemination or consumption of user
information; andcomparing the dissemination or consumption against a
performance benchmark to establish a performance rating for a digital
representation of the social network.

10. The method of claim 9, further comprising employing a social network
benchmark or an enterprise network benchmark as the performance
benchmark.

11. The method of claim 9, further comprising generating the performance
benchmark based on a data dissemination rate, a task completion rate or a
user interaction rate, accomplished via the communication network.

13. The method of claim 12, further comprising scoring the dissemination
or consumption of information for each message analyzed or for groups of
messages related to a common set of tasks, project groups, organizations
or social groups.

14. The method of claim 9, further comprising scoring data disseminated
top-down from management or spread virally throughout the network.

15. The method of claim 9, further comprising storing performance ratings
of a plurality of different social or enterprise networks as network
benchmarks.

16. The method of claim 15, further comprising comparing the performance
rating of the social network with a performance rating of at least one of
the plurality of networks.

18. The method of claim 9, further comprising identifying changes in the
inter-personal relationships and updating the digital representation or
the performance benchmark according to the changes.

19. The method of claim 9, further comprising:identifying a performance
disparity between the performance rating and the performance
benchmark;determining a change to the composition of the communication
network, the inter-personal relationships or the digital representation
of the social network to improve the disparity; andoutputting the
determined change to a user of the communication network.

20. A network performance characterization system, comprising:a memory
that obtains and stores data descriptive of a subset of inter-personal
relationships of a set of users of a communication network, the subset of
inter-personal relationships comprise a social network;a tracking
component that processes data transmitted via the communication network
to monitor dissemination or consumption of the data by user devices;an
analysis component that determines a task completion rate as a function
of the dissemination or consumption of the data; anda rating component
that compares the task completion rate against a performance benchmark to
establish a performance rating for a digital representation of the social
network.

Description:

BACKGROUND

[0001]Integrated network communications have provided significant advances
in social and enterprise activities. On the enterprise side, efficiencies
with which individuals can share information, perform tasks, disseminate
instructions, search for knowledge-based resources, expose data to users,
or share user concerns have greatly increased by advantages provided by
inter-personal networks. In regard to social networks, user
inter-connectivity and inter-relatedness have been increased as social
networking websites, such as Facebook.com, Twitter.com, LinkedIn.com, and
so on, have enabled users to share personal information, media files,
media applications, pictures, videos, audio, and so on, over the
Internet.

[0002]In addition to the foregoing, e-mail and other electronic messaging
systems have enabled a technical revolution in business and personal
communications, and have provided a platform for integrated social and
organizational networking. In recent years, use of electronic messaging,
such as e-mail, short messaging, text messaging, blogging, electronic
forums, and so on, has increased exponentially due to the inexpensive and
near instantaneous communication platform that electronic messaging
provides. Such platforms have rapidly decreased the time required to
share and disseminate information, whether for a large, multi-national
organization, a network of friends or family members, or remotely located
small business partners.

[0003]Building upon the powerful infrastructure of electronic network
communications, electronic social networks provide an enhanced ability
for individuals to interact and share information utilizing such
infrastructure. Additionally, electronic social networks can be
implemented in small, private networks, large corporate networks, as well
as publicly on the Internet. Users of such networks can perform various
communication tasks, such as planning social events, inviting friends to
parties, preparing a business meeting, comparing shared investment
strategies, and the like. As interactivity and flexibility of social
networks continue to increase, the possibilities of applications
springing forth from such advancements may open new horizons and break
new paradigms in inter-personal and enterprise communications and
activities.

SUMMARY

[0004]The following presents a simplified summary in order to provide a
basic understanding of some aspects of the claimed subject matter. This
summary is not an extensive overview. It is not intended to identify
key/critical elements or to delineate the scope of the claimed subject
matter. Its sole purpose is to present some concepts in a simplified form
as a prelude to the more detailed description that is presented later.

[0005]The subject disclosure provides for characterizing and determining
effectiveness of social networks. Data descriptive of inter-relationships
of persons can be employed to generate a social connectivity map for
users of a communication network. Data disseminated or consumed via the
communication network can be monitored and characterized in conjunction
with task performance. The characterization can be compared with a
performance benchmark to rate a social network of individuals, or the
communication network, in effecting user tasks or other user activities.
Accordingly, individuals and organizations can determine and compare the
effectiveness of a network in assisting user activities with various
predetermined benchmarks.

[0006]According to some aspects of the subject disclosure, optimizing a
communication network or digital representation of a social network
(e.g., an inter-personal connectivity map managed in an electronic
database) can be accomplished. Disparities between a performance
benchmark and a network under test can be determined and used to identify
deficiencies in the network. In some such aspects, recommendations can be
output to a user of the network, highlighting changes to network
composition to overcome the deficiencies. According to still other
aspects, optimization can be analyzed against a set of performance
benchmarks to identify impact of proposed changes on other activities,
functions or aspects of the network that might be affected by recommended
changes. The analysis can also be output to the user to give a
comprehensive effect that the changes might have on the network.

[0007]According to one or more additional aspects, the subject disclosure
provides for importable/exportable performance benchmarks for rating
social networks, or for rating communication networks that provide an
electronic interface for members of the social networks. Performance
benchmarks can be written as exportable files that can be shared among
user communication devices (e.g., computers, laptops, mobile phones,
etc.). Such other devices can import the exported files in order to load
a benchmark into a particular system or network. Additionally, the
benchmarks can be trained or customized to needs of a particular
individual, group or enterprise. Thus, as one example, a company could
generate a benchmark as a particular standard for its networks, and
export the benchmark file for other divisions of the organization to
standardize their networks against. Accordingly, network performance
benchmarks can be programmable, enabling extensibility and resulting in a
dynamic ecosystem for rating or standardizing social and communication
networks.

[0008]The following description and the annexed drawings set forth in
detail certain illustrative aspects of the claimed subject matter. These
aspects are indicative, however, of but a few of the various ways in
which the principles of the claimed subject matter may be employed and
the claimed subject matter is intended to include all such aspects and
their equivalents. Other advantages and distinguishing features of the
claimed subject matter will become apparent from the following detailed
description of the claimed subject matter when considered in conjunction
with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 depicts a block diagram of an example system that provides a
performance rating for social networks according to aspects of the
subject disclosure.

[0010]FIG. 2 illustrates a block diagram of a sample system for
characterizing performance of a social network in assisting user activity
according to further aspects.

[0011]FIG. 3 depicts a block diagram of an example system for representing
inter-personal relatedness as an electronic social network according to
some aspects.

[0012]FIG. 4 illustrates a block diagram of an example system that employs
importable benchmark files for rating aspects of a social network.

[0013]FIG. 5 depicts a block diagram of an example system that aggregates
and characterizes social networks maintained among multiple network
platforms.

[0014]FIG. 6 depicts a block diagram of a sample system that facilitates
optimization of social network composition according to particular
disclosed aspects.

[0015]FIG. 7 illustrates a flowchart of an example methodology for rating
performance of social networks according to other aspects of the subject
disclosure.

[0016]FIGS. 8 and 9 depict flowcharts of an example methodology for
characterizing, rating and optimizing a social network according to some
aspects.

[0017]FIG. 10 illustrates a block diagram of a suitable operating
environment for implementing various aspects of the subject disclosure.

[0019]The claimed subject matter is now described with reference to the
drawings, wherein like reference numerals are used to refer to like
elements throughout. In the following description, for purposes of
explanation, numerous specific details are set forth in order to provide
a thorough understanding of the claimed subject matter. It may be
evident, however, that the claimed subject matter may be practiced
without these specific details. In other instances, well-known structures
and devices are shown in block diagram form in order to facilitate
describing the claimed subject matter.

[0020]As used in this application, the terms "component," "module,"
"system", "interface", "engine", or the like are generally intended to
refer to a computer-related entity, either hardware, a combination of
hardware and software, software, or software in execution. For example, a
component may be, but is not limited to being, a process running on a
processor, a processor, an object, an executable, a thread of execution,
a program, and/or a computer. By way of illustration, both an application
running on a controller and the controller can be a component. One or
more components may reside within a process and/or thread of execution
and a component can be localized on one computer and/or distributed
between two or more computers. As another example, an interface can
include I/O components as well as associated processor, application,
and/or API components, and can be as simple as a command line or a more
complex Integrated Development Environment (IDE).

[0021]Communication networks have become powerful tools for sharing
knowledge and experience in social settings as well as business settings.
Currently, such networks can provide real-time dissemination of
information, at almost any distance around the globe. Networks can be
public, like the Internet and World Wide Web, or private, such as
personal or business networks requiring authorized access to a limited
subset of users. Furthermore, communication networks can employ wireless
device access or fixed-line device access, or both. Additionally,
sub-networks can exist within a larger network, such as a domain or
sub-domain, having particular applications and application features,
settings or preferences local to the sub-network. Accordingly, by
selectively configuring a sub-network, distinctiveness is achieved, both
in displaying information to a user, providing access to the user and in
facilitating user control over various user-oriented applications.

[0022]Recent applications for communication networks include
electronically characterizing human groups and organizations and
providing a means of electronic communication between members thereof.
Human interactions and relationships, termed social networks, include
families, groups of friends, business and investment partners, instant
message `buddies`, members of for profit and non-profit organizations,
and the like. In one characterization of inter-personal relationships,
individual persons are represented as nodes of a network, and ties
between the nodes are based on various interactions and communications
between the persons. Each person, or node, is directly connected to
others whom the person has direct interaction with. Such person is
indirectly connected with other persons, whom their direct contacts have
direct interaction with, and still other persons who their direct
contacts have indirect interaction with (through one or more other
persons), and so on. Thus, in such a characterization of inter-personal
relationships, a social network is analogous to a large web of
interconnected person-nodes.

[0023]By storing node and connectivity data electronically, for instance
in a database that tracks individual persons and their direct and
indirect relationships, an underlying web of inter-personal relationships
can generate an electronic social network. Some electronic social
networks are maintained on Internet web sites, including sites such as
Facebook®, Twitter®, LinkedIn.com®, or the like. In addition,
many corporations include electronic social networks maintained on
private intranets, and some private individuals and businesses also
maintain electronic social networks on various public and private
networks. Electronic social networks that enable individuals to post or
share data and media (e.g., photographs, videos, audio recordings, text,
blogs, and the like) pertaining to their personal or business interests,
hobbies, areas of expertise, research, political views, business
ventures, investment portfolios or interests, and so on. In addition, an
underlying communication network (e.g., Internet, intranet, mobile
communication network, private network) supporting an electronic social
network can facilitate electronic communication and data exchange between
user nodes of such a social network, in the form of instant message (IM),
short message service (SMS), e-mail, voice communication (e.g., voice
over Internet Protocol [VoIP], or circuit-switched voice), or other forms
of electronic communication. To interact with other network users or with
network components supporting the social network, a communication device,
such as a computer, mobile phone, laptop, personal digital assistant
(PDA), or like electronic device is employed by a network user. Thus, the
electronic device provides an interface to the electronic social network
and consequently with other network users.

[0024]One use for electronic social networks in enterprise is to connect
individuals having various experience and expertise on projects and tasks
of the organization. Thus, employees can identify individuals having
experience in a particular field or on a particular task. Data can be
exchanged between such users to effect or guide performance of the task.
In addition, enterprise management can disseminate instructions
throughout an enterprise, or to selected divisions, workgroups or members
thereof, via the electronic social network. Moreover, users can spread
information virally, from user to user, employing e-mail, IM or other
mass electronic communication mechanisms. The electronic social network
therefore can serve as a useful tool in conducting enterprise activities
and accomplishing tasks, by disseminating instructions or coupling users
of the enterprise.

[0025]Although significant benefit can be achieved through electronic
social networks, optimizing those benefits can be tedious and time
consuming. For instance, network administrators might have to manually
collect feedback from users of the network to determine how much
assistance the network provides for user tasks, and how efficient that
assistance is. Furthermore, identifying changes in the network to
optimize one set of interactions can affect, sometimes adversely, other
sets of interactions between various users. The larger the electronic
social network, the more difficult manual optimization can become, and
the more likely that optimizing one aspect of the network detracts from
another aspect. Accordingly, a comprehensive mechanism for rating
performance of a network and optimizing composition of or infrastructure
supporting the electronic social network can provide significant accuracy
and efficiency in such optimizations.

[0026]To address these or like problems, the subject disclosure provides
for rating performance of an electronic social network. In some aspects,
dissemination or consumption of data can be analyzed to determine how
network infrastructure supporting an electronic social network performs.
Additionally, the dissemination or consumption of data can be analyzed to
infer user habits, preferences or predispositions toward network
applications, network interface devices, select tasks or sets of tasks,
or other users of the network. Success of the network can be
characterized by monitoring user tasks, activities, feedback and
communications, and comparing results of such tasks, activities, etc.,
with a performance benchmark. The comparison can be quantified or
qualified as a function of the dissemination or consumption of data, as a
standard for the electronic social network. Based on such comparisons, a
performance rating can be given for the electronic social network or
supporting infrastructure, as a means of grading the network based on the
performance benchmark.

[0027]In additional aspects of the subject disclosure, analysis of task
performance and data dissemination/consumption can be utilized to
optimize composition of the network. Latencies in dissemination or
consumption of data can be identified and referenced against a
performance benchmark to identify bottlenecks. Such bottlenecks can be
characterized by rates at which users consume information provided by
others, respond with information, complete tasks, and the like.
Furthermore, the rates can be referenced as a function of network user,
user interface device, interface or communication application, network
group, division or team, network infrastructure involved in transferring
information among users, time of day, day of week, period of a year, and
so on. A bottleneck can be identified where data dissemination or
consumption rates fall below benchmark levels, or levels at which like
users or groups of users perform. Thus, characterization of network
performance can comprise a comprehensive map of what data is disseminated
over the network and when, how well it is disseminated, how users act
upon the data, results of such actions, or combinations thereof.

[0028]Upon identifying bottlenecks in sharing or consuming data, or in
task performance, recommendations can be made to improve the electronic
social network. In some aspects, the recommendations could comprise
altering composition of the network (e.g., reorganizing user nodes),
recommending particular tasks for particular users, recommending other
users having a particular expertise for a particular task, recommending
changes in infrastructure (e.g., a low bandwidth router or server) to
speed dissemination of data, or the like. The recommendations can also be
made based on benchmark electronic social network compositions, compared
with composition of a network under test. Thus, in addition to rating
performance of an electronic social network, improving such a network to
more efficiently support user activity is also disclosed.

[0029]To increase flexibility and usefulness of the foregoing, also
disclosed are exportable/importable performance benchmarks that can be
shared among networks or among individuals and organizations. A default
benchmark can be trained on a default network comprising a default set of
users. The benchmark can be written as an exportable file, and imported
to another network, or to another division of the network. Thus, for
instance, one division of an enterprise operating in a first country can
be utilized to generate a social network benchmark, and another division
operating in a second country can be analyzed with respect to the
benchmark. Thus, the enterprise can employ the exportable benchmark to
compare or standardize operations across disparate networks, even in
remote locations. According to at least some aspects, exportable
benchmarks can be sold, leased, etc., to other organizations or
individuals as standards for an electronic social network. Thus, a new
network could be optimized and brought to speed with an existing network,
reducing time required to tune a network to needs of an organization.

[0030]It should be appreciated that, as described herein, the claimed
subject matter may be implemented as a method, apparatus, or article of
manufacture using standard programming and/or engineering techniques to
produce software, firmware, hardware, or any combination thereof to
control a computer to implement the disclosed subject matter. The term
"article of manufacture" as used herein is intended to encompass a
computer program accessible from any computer-readable device, carrier,
or media. For example, computer readable media can include but are not
limited to magnetic storage devices (e.g., hard disk, floppy disk,
magnetic strips . . . ), optical disks (e.g., compact disk (CD), digital
versatile disk (DVD) . . . ), smart cards, and flash memory devices
(e.g., card, stick, key drive . . . ). Additionally it should be
appreciated that a carrier wave can be employed to carry
computer-readable electronic data such as those used in transmitting and
receiving electronic mail or in accessing a network such as the Internet
or a local area network (LAN). The aforementioned carrier wave, in
conjunction with transmission or reception hardware and/or software, can
also provide control of a computer to implement the disclosed subject
matter. Of course, those skilled in the art will recognize many
modifications may be made to this configuration without departing from
the scope or spirit of the claimed subject matter.

[0031]Moreover, the word "exemplary" is used herein to mean serving as an
example, instance, or illustration. Any aspect or design described herein
as "exemplary" is not necessarily to be construed as preferred or
advantageous over other aspects or designs. Rather, use of the word
exemplary is intended to present concepts in a concrete fashion. As used
in this application and the amended claims, the term "or" is intended to
mean an inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise, or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That is, if X
employs A; X employs B; or X employs both A and B, then "X employs A or
B" is satisfied under any of the foregoing instances. In addition, the
articles "a" and "an" as used in this application and the appended claims
should generally be construed to mean "one or more" unless specified
otherwise or clear from context to be directed to a singular form.

[0032]As used herein, the terms to "infer" or "inference" refer generally
to the process of reasoning about or inferring states of the system,
environment, and/or user from a set of observations as captured via
events and/or data. Inference can be employed to identify a specific
context or action, or can generate a probability distribution over
states, for example. The inference can be probabilistic--that is, the
computation of a probability distribution over states of interest based
on a consideration of data and events. Inference can also refer to
techniques employed for composing higher-level events from a set of
events and/or data. Such inference results in the construction of new
events or actions from a set of observed events and/or stored event data,
whether or not the events are correlated in close temporal proximity, and
whether the events and data come from one or several event and data
sources.

[0033]Turning now to the figures, FIG. 1 depicts a block diagram of an
example system 100 that can rate performance of an electronic social
network according to aspects of the subject disclosure. System 100 can
comprise a network performance system 102 that obtains data descriptive
of inter-personal relationships of a set of individuals. Such data can be
obtained at a data store 106 and stored in a relationship data file 108.
In some aspects, data store 106 can comprise long-term or permanent
storage, such as a disc or disk drive, hard drive, database, data storage
server, or the like. In other aspects, the data store 106 can be
temporary data storage, such as random access memory (RAM), Flash memory,
cache memory, or the like.

[0034]According to some aspects of the disclosure, the relationship data
file 108 stores the inter-personal relationships as a web of user nodes,
connected by various direct and indirect interactions with other user
nodes. The interactions can comprise direct personal communications
(e.g., face-to-face communication, electronic communication, voice
communication, etc.) as well as indirect communications (e.g.,
information posted on a website, blog, or the like, that is downloaded or
shared with another). The data an be collected by electronic means, such
as a search engine, data mining component or the like, or by user input,
such as database entry, file upload, text entry, voice entry (e.g.,
converted to text via speech-to-text processors employing language
processing), and so on. Additionally, the data (108) can comprise
annotated information, metadata, or other suitable information tags,
providing user-node identity or pseudo-identity (e.g., chat room or
e-mail handle), expertise, interests, hobbies, experiences of such
user-nodes, as well as the type, or quality of the inter-node
interactions (e.g., whether electronic communication, face-to-face
communication, chat buddies, group-plan cell phone users), or the content
or context thereof (e.g., topic of discussion, expertise or experience
shared, transcript of communication, etc.).

[0035]The relationship data file 108, describing the inter-personal
relationships of the set of individuals, is provided to the network
performance system 102 from data store 106. Network performance system
102 can comprise a tracking component 104 that processes data transmitted
over a communication network (not depicted, but see FIGS. 2, 3 and 5,
infra). The processed data pertains to information disseminated over an
electronic social network associated with the inter-personal relationship
data included in the relationship data file 108. In some aspects, the
communication network is communicatively coupled with the electronic
social network, which is comprised of a subset of the inter-personal
relationship. In other aspects, the communication network includes
infrastructure that supports (e.g., server, data store, database, query
engine, search engine, etc.) the electronic social network. According to
particular aspects of the subject disclosure, tracking component 104
monitors dissemination or consumption of data by user devices (not
depicted, but see FIGS. 2, 3 and 5, infra) coupled with the communication
network. For instance, the tracking component 104 can analyze data
compiled, transmitted or received at user communication applications on
such user devices. The applications can include an SMS, IM, e-mail, text
message, website, blog, really simple syndication (RSS), voice-to-text,
media capture, or electronic sensor application providing a user
communication/data interface to the communication network. Additionally,
tracking component 104 can monitor network infrastructure (e.g., servers,
routers, databases, query engines) facilitating data transfer between the
user devices. Accordingly, a comprehensive analysis of the dissemination
and consumption of data via the communication network can be achieved by
tracking component 104.

[0036]The dissemination/consumption of data measured by tracking component
104, along with the relationship data (108) can be provided to a rating
component 110. Rating component employs the data
consumption/dissemination to characterize performance of an electronic
social network represented by the relationship data (108). The
characterization can be compared with a performance benchmark 112, which
includes benchmark statistics for dissemination or consumption of data on
a benchmark electronic social network. Based on the comparison, rating
component 110 can generate and generate a performance rating for the
electronic social network represented by the relationship data (108).
Accordingly, network performance system 102 can rate the effectiveness in
which the electronic social network assists users in obtaining and
sharing data with other users of the network.

[0037]FIG. 2 depicts a block diagram of an example system 200 that
facilitates characterization of network-related or device-related user
activities and effectiveness of an electronic social network (202) in
assisting with those activities. Various user-network interface devices
can capture information pertinent to the user activities, which can be
analyzed to establish a baseline characterization of user activity
performance. The activity performance can be cross-referenced against
data dissemination, data consumption or like analysis for the electronic
social network (202). The baseline characterization, data analysis and
results of the comparison can be output to characterize an impact of the
electronic social network on user activity.

[0038]System 200 comprises one or more communication networks 202
facilitating electronic interaction between one or more users 206 of the
network, employing one or more user devices 204 as a user-network
interface. The communication network(s) 202 can comprise various suitable
platforms for remote communication between electronic devices (e.g., see
FIG. 11, infra), including fixed line communication networks (e.g., cable
line, digital subscriber line [DSL], broadband over power line, Ethernet,
or like wired communication interfaces comprising a suitable
inter-communication protocol such as transport control protocol/Internet
Protocol [TCP/IP] or the like), wireless communication networks (e.g.,
wireless local area networks [WLANs] such as 802.11a, b, c, d, e, g, h,
n, . . . , etc. protocol networks, wireless wide area networks [WWANs],
licensed cellular networks, wireless interoperability for microwave
access [WiMAX] networks, and so on), or a suitable combination thereof.
Additionally, communication network(s) 202 can include, or be directly or
indirectly coupled with, infrastructure supporting an electronic social
network, as described herein.

[0039]User interfaces (204) to the communication network(s) 202 can
comprise various suitable devices 204, such as desktop computers, laptop
computers, mobile communication devices, mobile phones, network-capable
gaming devices, PDAs, and so on. Additionally, such devices 204 can
comprise one or more user interface applications or systems that
facilitate data exchange between a device 204 and the network(s) 202, or
between devices 204. Such interface applications or systems can include
e-mail, IM, SMS, operating system executables, voice-to-text or
text-to-voice applications, web browser, RSS reader, RSS aggregator, or
various other suitable applications or system components enabling data
(e.g., text, media, voice, etc.) to be exchanged between a user device
204 and the network(s) 202.

[0040]To characterize user activity and user activity performance, system
200 can comprise activity capture components (208A, 208B, 208C) that can
obtain, record or output information pertaining to human activity. The
information can comprise video data processed by a video capture
component 208A (e.g., a camera, video camera, web cam), audio data
captured by an audio capture component 208B (e.g., microphone), tactile
data processed by tactile or haptic sensors (208C), or other biometric
information obtained from biometric sensors, such as infrared sensors
(e.g., to measure body or surface temperature), heart rate or blood
pressure sensors (e.g., to infer anxiety, emotion disposition or tension
from heart rate, blood pressure or rates of changes therein), video
camera (208A) to identify sweating, measure pupil dilation, and so on.
Various user states, emotional dispositions or physical activities can be
inferred from sensor data obtained from the various sensors 208A, 208B,
208C. In addition, users 206 can provide information pertaining to user
activity (e.g., type of activity, name of activity, one or more goals,
progress toward a goal, bottleneck in the progress, identity of users
having experience or expertise pertinent to the activity, and so on)
through manual data entry (e.g., whether text, voice or tactile based)
onto one or more user devices 204. Information descriptive of user
activities and states of such activities can be utilized to define user
tasks against which performance of the electronic social network can be
measured.

[0041]System 200 can further comprise an analysis component 210 that
receives the data descriptive of user activities, as well as an analysis
of an electronic social network that impacts implementation,
effectiveness or efficiency of such activities. Analysis component 210
can parse and quantify/qualify received data in order to establish a
performance characteristic 220 for the communication network or
electronic social network. For instance, a media processor 212 can
analyze audio, video, tactile, or other sensory-related media data
provided by sensors 208A, 208B, 208C to identify user activities and user
dispositions or emotional states with respect to such activities.
Furthermore, a weighting component 214 can employ language processing to
analyze communications sent by the user to other users of the
communication network(s) 202. Content of such communications pertinent to
identified activities can be compiled and scored based on relativity to
implementation, effectiveness or efficiency of the activities. An
aggregation component 216 can combine compiled scores to further
characterize or assist in optimization of (e.g., see below) a state of
the user with respect to the activity, or a state of the activity (e.g.,
started, completed, degree of progress, problems encountered in progress)
itself.

[0042]In addition to the foregoing, analysis component 210 can analyze
data exchanged over the communication network(s) 202 or electronic social
network to determine effectiveness in implementing various user
activities. Rates at which activities are initiated, progress or are
completed, or rates with which data pertaining to an activity is
disseminated or consumed via the network, or rates with which users
communicate or collaborate can be determined by analysis component 210.
The determined rates can be correlated with the user activities or states
of activities, to characterize the impact of the social network or
communication network(s) 202 on such activities. The characterization is
output as a performance characterization file 220 for analysis by a
network performance rating system, as described herein (e.g., see FIG. 1,
supra).

[0043]According to at least some aspects of the subject disclosure,
analysis component can employ a machine learning and optimization
component 218 to optimize data analysis over time and over multiple
iterations of analyzed data. For instance, accurately characterizing or
identifying a task based on captured media data can be one example of
optimization. Another example can comprise analyzing user communications
and correlating the communications with activity-related data. Still
other examples can comprise correlating user activities with network
performance factors (e.g., data dissemination rates) to generate an
overall characterization of an impact of an electronic social network in
supporting user activities.

[0044]In order to optimize data analysis, machine learning and
optimization component 218 can utilize a set of models (e.g., user
interface model, text-to-speech or speech-to-text models, user biometric
response model, user disposition-physical response model, language
processing model, inter-user interaction model, statistical models based
on the foregoing, etc.) in connection with determining or inferring user
tasks and impact of the social network on such tasks. The models can be
based on a plurality of information (e.g., media capture data, manual
data entry, cross-network communication, etc.). Optimization routines
associated with machine learning and optimization component 218 can
harness a model(s) that is trained from previously collected data, a
model(s) that is based on a prior model(s) that is updated with new data,
via model mixture or data mixing methodology, or simply one that is
trained with seed data, and thereafter tuned in real-time by training
with actual field data based on parameters modified as a result of error
correction instances.

[0045]In addition, machine learning and optimization component 218 can
employ machine learning and reasoning techniques in connection with
making determinations or inferences regarding optimization decisions,
such as correlating data dissemination/consumption statistics with user
activity performance, across a plurality of users and device/network use
contexts of such users. For example, machine learning and optimization
component 218 can employ a probabilistic-based or statistical-based
approach in connection with identifying and/or updating a user
disposition, physical/emotional state or activity state based on previous
biometric sensor data collected for the user, or similar data collected
for a plurality of similar users. Inferences can be based in part upon
explicit training of classifier(s) (not shown), or implicit training
based at least upon one or more monitored results, and the like.

[0046]Machine learning and optimization component 218 can also employ one
of numerous methodologies for learning from data and then drawing
inferences from the models so constructed (e.g., Hidden Markov Models
(HMMs) and related prototypical dependency models, more general
probabilistic graphical models, such as Bayesian networks, e.g., created
by structure search using a Bayesian model score or approximation, linear
classifiers, such as support vector machines (SVMs), non-linear
classifiers, such as methods referred to as "neural network"
methodologies, fuzzy logic methodologies, and other approaches that
perform data fusion, etc.) in accordance with implementing various
aspects described herein. Methodologies employed by optimization module
310 can also include mechanisms for the capture of logical relationships
such as theorem provers or heuristic rule-based expert systems.
Inferences derived from such learned or manually constructed models can
be employed in other optimization techniques, such as linear and
non-linear programming, that seek to maximize probabilities of error. For
example, maximizing an overall accuracy of correlations between social
network composition or capabilities and user activity performance can be
achieved through such optimization techniques.

[0047]FIG. 3 depicts a block diagram of an example system 300 that can
transform a human social network into an electronic representation of
such network. System 300 can obtain inter-personal relationship data of a
set of persons. Such data can be utilized to generate a social
connectivity map 312, showing direct or indirect relationships between
persons and, in some aspects, characterizing those relationships based on
past, current or anticipated interactions. The social connectivity map
312 can then be programmed or stored onto communication network
infrastructure components to yield an electronic social network. Such
network can facilitate mass communication between users, characterization
of inter-personal relationships, provide visual depiction of personal
activity, aid in aggregating and maximizing human resources, and the
like.

[0048]System 300 can comprise a communication network(s) 302 providing a
platform for remote data exchange between one or more electronic
communication devices 304. Users 306 of such devices 304 can be
identified (whether single or multiple users per device, or multiple
devices per user) and established as user nodes for a social network.
Other persons with which the user nodes are associated with, either
through direct personal interaction (e.g., direct node associations) or
the personal interactions of other persons (e.g., indirect node
associations), form various user relationship networks 308A, 308B, 308C,
which can be integrated into the social network. Interactions between the
users and their direct/indirect associates, as well as interactions among
the associates themselves, whether direct or indirect, can be
characterized in the social network. For instance, as depicted,
user2 has two direct relationships with two persons, but four
additional indirect relationships. The indirect relationships based on a
direct relationship between one such person and another person, and the
direct and indirect relationships of that other person, which include
userN (where N is a positive integer). User1 has three direct and
indirect relationships, but none that interface with person-nodes
associated with user2 or userN. Thus, user1 has no direct or
indirect relationship with user2 or userN, unless an
interaction is established via the communication network(s) 302.

[0049]Data pertaining to personal inter-relationships can be provided by
users (e.g., by uploading data to a network database--not depicted), or
mined by a data mining component 310, or a combination of both. Data
entry can include uploading media (e.g., pictures, video, audio, etc.) or
other application files to the network(s) 302, posting text on a website
or forum, transmitting or receiving an electronic message to/from or
copying a person, or the like. Data mining component 310, on the other
hand, can automate data compilation pertaining to users and user
identity, history, background, interests, hobbies, experiences, etc.
Thus, for instance, data mining component 310 can search multiple
networks (e.g., see FIG. 5) for information pertaining to a set of
network users, or interactions between such users or other persons (who
may or may not be users of the network 302). Thus, for instance, data
mining component 310 might search public social networking sites (e.g.,
Facebook, Twitter, LinkedIn.com, and so on) for users and user
interactions, private networks maintaining private social networking
applications, as well as traditional communication networks, such as
e-mail servers, IM or SMS servers, mobile communication networks, etc.,
to find users and identify interactions, whether electronic (and, e.g.,
memorialized in a network component) or face-to-face (and, e.g.,
memorialized in communication or uploaded data). Based on the identified
user nodes and interactions among such nodes, data mining component 310
can generate a social connectivity map file 312, descriptive of such user
nodes and interactions. The map file 312 can be output to a network
performance analysis system (e.g., see FIG. 1, supra), as described
herein.

[0050]FIG. 4 depicts a block diagram of an example system 400 that can
employ a set of network performance benchmarks to rate performance of an
electronic social network. The performance benchmarks can comprise
exportable/importable files that can be swapped in or out of a network
performance system, enabling analysis of various aspects or functions of
the social network, various users or user groups, various tasks or sets
of tasks, or the like. In addition to the foregoing, such benchmark files
can enable a social network of one organization or individual to be
measured against another social network of a different
organization/individual or group of organizations/individuals.
Accordingly, an ecosystem of benchmarks can be provided, enabling
electronic social networks to be analyzed in parts (e.g., by task(s),
user(s), division(s), team(s), time period(s), etc.) or in whole,
enabling trading, lease or sale of network benchmarks among various
organizations, possibly resulting in additional streams of revenue for
operators or purveyors of an electronic social network.

[0051]System 400 can comprise a network performance system 402 that rates
performance of an electronic social network in assisting users of such
network in accomplishing tasks and activities. To rate the network
performance, performance system 402 can generate a correlation between
dissemination/consumption of information associated with the social
network and performance of the tasks/activities. A result of the
correlation provides an impact of the social network on the user
tasks/activities. This impact can be compared with one or more
performance benchmarks to rate the performance of the social network.

[0052]According to some aspects of the subject disclosure, network
performance system 402 can select among various aspects, functions,
compositions, tasks, users or user groups of the social network for
testing. In such aspects, a particular performance benchmark providing a
standard performance for the selected network attribute can be requested
by the network performance system 402. In other aspects, the network
performance system 402 can select among various benchmark targets,
provided by a desired benchmark organization, trained on a particular set
of users (e.g., a set of expert users), trained on a particular
electronic social network or type of such network, or the like. Once a
desired benchmark or benchmark type is identified, network performance
system sends a benchmark request to a selection component 404. Selection
component 404 can reference a list of benchmark files 406 stored at a
data store 408, to identify the requested benchmark, or where suitable, a
most-similar benchmark file. If a suitable benchmark file is identified,
selection component returns the selected benchmark file 410 to the
network performance system. Based on the network characterization and
selected benchmark file 410, network performance system can output a
performance rating that is particular to a selected aspect or function of
an electronic social network, or standardized against a desired target
social network.

[0053]FIG. 5 depicts a block diagram of a sample system 500 that provides
comprehensive data analysis of disparate networks to characterize and map
a social network(s) for performance evaluation. System 500 can be
implemented across various communication networks and electronic social
networking platforms. In addition, system 500 can generate a performance
characterization and social network connectivity map that can be output
to a network performance system, as described herein. Accordingly, system
500 can serve as a powerful and flexible front-end for such a network
performance system, enabling the performance system to be plugged-in to
various networks and network infrastructures.

[0054]System 500 can comprise a plurality of communication networks,
network1 502A through networkN 502B, coupled by one or more
network gateways. It should be appreciated that the networks (502A, 502B)
can comprise various suitable wired and wireless networks, such as the
Internet, WLAN, WWAN, cellular network, WiMAX network, and so on.
Additionally, the network gateway(s) 506 can comprise various suitable
gateways configured to route and translate information sent between
various networks, of disparate types or maintained by disparate
operators.

[0055]Additionally, system 500 can comprise a set of network devices 504A,
504B coupled with the respective communication networks 502A, 502B. Such
devices can include, for instance, user interface devices for enabling
users to exchange data with one or more of the networks (502A, 502B) or
other network devices (504A, 504B). Additionally, the network devices
504A, 504B can comprise infrastructure components, such as network
servers routers, gateways, hubs, switches, databases, data stores, and
the like, providing network functionality and storing and managing
network applications and data.

[0056]According to particular aspects of the subject disclosure, system
500 can comprise one or more data stores (504A, 504B) that include data
descriptive of inter-personal relationships of a set of users of one or
more of the communication networks 502A, 502B. The descriptive data can
be uploaded to the data stores by various mechanisms. Such mechanisms can
include user data entry, as well as search/analysis and storage by other
network components.

[0057]In addition to the foregoing, system 500 can comprise a data mining
component 508 that searches, queries, snoops, etc., the user devices
504A, 504B to identify users of the communication network, and other
persons associated with such users. When a user is identified, data
mining component 508 can search for information pertaining to the user to
identify hobbies, interests, expertise, experiences, profession, etc.,
associated with such user. Additionally, the data mining component 508
can attempt to identify other persons, identities of such persons, and
contextual information 9 e.g., hobbies, interests, etc.), associated with
such persons. Furthermore, the data mining component can search for
information characterizing interactions between the user and such
persons, to annotate or mark the user inter-relationships.

[0058]Data obtained by the data mining component 508 is output as a social
connectivity map 510, which can be stored in a map file 514 maintained at
a connectivity database 512. In some aspects of the subject disclosure,
the social map 510 can characterize identified communication network
users as user nodes and interactions between users and other identified
persons as direct and indirect links between the user nodes. Additional
contextual information pertaining to users and user node interactions can
be annotated to the user nodes/interactions (e.g., as metadata, data
tags, or other suitable annotation mechanism), to provide rich contextual
information for the social connectivity map 510. The map 510, stored in
the map file 514, can be provided to a network performance system for
analysis of the composition of the inter-personal connectivity in
conjunction with disseminating or consuming data pertaining to user
activities and tasks, as described herein.

[0059]In addition to the foregoing, system 500 can comprise an analysis
component 516 that monitors data, descriptive of user activities,
generated at various user devices 504A, 504B to characterize user tasks
and activities, states thereof, and dispositions of users with respect to
such tasks or activities. Additionally, the analysis component 516 can
monitor dissemination/consumption of data among the communication
networks 502A, 502B or devices 504A, 504B as a function of user
tasks/activities to characterize effectiveness of the communication
networks 502A, 502B in supporting the user tasks and activities. Thus,
for instance, the analysis component 516 can characterize implementation,
effectiveness or efficiency of the tasks/activities as a function of the
ability of social network applications to disseminate data among users,
pair users or sets of users as a function of common interests, goals,
tasks, etc., or facilitate consumption of data pertinent to a
task/activity. The analysis component 516 can store the characterizations
in a performance characterization file 520 on a performance database, for
use by a network performance system as described herein.

[0060]FIG. 6 depicts a block diagram of an example system 600 facilitating
optimization of an electronic social network according to aspects of the
subject disclosure. System 600 can identify disparities in composition of
a social network, or infrastructure supporting an electronic
representation of such network, and recommend changes to the
composition/infrastructure to improve performance of the network.

[0061]System 600 can comprise a machine learning and optimization
component 602 that obtains a performance characterization 604 of an
electronic social network(s) and a social connectivity map descriptive of
personal inter-relationships of a set of users of the network(s). The
machine learning component 602 can analyze the performance
characterization 604 to first determine effectiveness of social
networking systems and applications in disseminating data throughout the
network, as a baseline measure of network efficiency. Furthermore, the
social connectivity map 606 can be analyzed for user node contextual
information pertinent to a task or activity, distance between user nodes
having pertinent information regarding a task, and the like. Thus, if a
user node contains metadata indicating an expertise in a particular task,
an expert association with the task can be generated for the user node.

[0062]Particular user nodes that obtain a large degree of support (e.g.,
in the way of communication responses, activity invitations, help
requests or data supportive of an activity) from other nodes of the
social network can be identified as network mavens, or influencers.
Network mavens can be categorized as a function of authority within the
organization, to characterize their influence based on authority, or
social factors (e.g., popularity, strength of personality or character,
etc.), or a combination thereof. The network mavens can be utilized as
problem solving resources of the social network (e.g., analogous to users
with task expertise), for implementing large user responses with
relatively small effort.

[0063]In addition, bottlenecks in network efficiency can be identified by
comparing the performance characterization or social connectivity map
with one or more performance benchmarks suited to a desired analysis.
Based on context and content of the social network, tasks or activities
in progress, and a desired performance analysis, machine learning and
optimization component 602 can identify changes to composition of a
social network (e.g., different clustering of user nodes), based on
social network resources (e.g., experts, mavens) having a likelihood of
impacting performance of a task or activity. The identified changes are
provided to an output component 612. Output component 612 can format the
identified social network changes to an output file format sufficient for
exposure to a user device. Optionally, the output file format can include
a network update format sufficient to change composition of the social
network according to parameters defined in an optimized output file 614.
Accordingly, recommendations made by system 600 can be automatically
implemented, by re-inserting the optimized output file 614 back into the
electronic social network system.

[0064]The aforementioned systems have been described with respect to
interaction between several components. It should be appreciated that
such systems and components can include those components or
sub-components specified therein, some of the specified components or
sub-components, and/or additional components. For example, a system could
include network performance system 102, analysis component 210, data
mining component 310, selection component 404 and machine learning and
optimization component 602, or a different combination of these and other
components. Sub-components could also be implemented as components
communicatively coupled to other components rather than included within
parent components. Additionally, it should be noted that one or more
components could be combined into a single component providing aggregate
functionality. For instance, data mining component 508 can include
analysis component 516, or vice versa, to facilitate generating a social
connectivity map and a performance characterization of a social network
by way of a single component. The components may also interact with one
or more other components not specifically described herein but known by
those of skill in the art.

[0065]Furthermore, as will be appreciated, various portions of the
disclosed systems above and methods below may include or consist of
artificial intelligence or knowledge or rule based components,
sub-components, processes, means, methodologies, or mechanisms (e.g.,
support vector machines, neural networks, expert systems, Bayesian belief
networks, fuzzy logic, data fusion engines, classifiers . . . ). Such
components, inter alia, and in addition to that already described herein,
can automate certain mechanisms or processes performed thereby to make
portions of the systems and methods more adaptive as well as efficient
and intelligent.

[0066]In view of the exemplary systems described supra, methodologies that
may be implemented in accordance with the disclosed subject matter will
be better appreciated with reference to the flow charts of FIGS. 7-9.
While for purposes of simplicity of explanation, the methodologies are
shown and described as a series of blocks, it is to be understood and
appreciated that the claimed subject matter is not limited by the order
of the blocks, as some blocks may occur in different orders and/or
concurrently with other blocks from what is depicted and described
herein. Moreover, not all illustrated blocks may be required to implement
the methodologies described hereinafter. Additionally, it should be
further appreciated that the methodologies disclosed hereinafter and
throughout this specification are capable of being stored on an article
of manufacture to facilitate transporting and transferring such
methodologies to computers. The term article of manufacture, as used, is
intended to encompass a computer program accessible from any
computer-readable device, device in conjunction with a carrier, or media.

[0067]FIG. 7 depicts a flowchart of an example methodology 700 for rating
performance of electronic social networks according to aspects of the
subject disclosure. At 702, method 700 can obtain data descriptive of
inter-personal relationships of a set of users of a communication
network. The inter-personal relationships can be characterized as a
social network of individuals, comprising users of the communication and
optionally non-users of the communication network, and indirect and
direct interactions between such individuals. Additionally, the
communication network can provide an infrastructure (e.g., database,
query server, memory, search engine, router, etc.) for maintaining an
electronic representation of the social network, and applications
providing electronic communication features for users of the
communication network.

[0068]At 704, method 700 can monitor dissemination or consumption of data
over the communication network. The dissemination or consumption of data
can be utilized to characterize effectiveness in sharing data among users
of the electronic social network. Additionally, the data can be monitored
to obtain or infer tasks or activities engaged in by one or more users.
In some aspects, characterization can be based on ability of network
applications to identify proper user nodes having expertise, experience,
social influence or authority to positively impact implementation,
effectiveness or efficiency of a task or activity. Alternatively, or in
addition, characterization can be based on likelihood of a set of users
to employ the electronic social network in solving a set of tasks. In yet
other examples, characterization can be based on a degree of affinity
with one or more interface applications or interface devices held by a
subset of users, and ability for the applications/devices to effectively
implement tasks or activities.

[0069]At 706, method 700 can compare the dissemination or consumption of
data to a social network performance benchmark to rate the electronic
social network or underlying communication network supporting the social
network, or both. The performance benchmark can provide a standard or
goal pertinent to one or more tasks, trained on a set of benchmark users,
expert users, or the like. By identifying a deficiency in performing or
implementing a set of tasks relative the performance benchmark, the
network rating(s) can be generated and output to a user of the network
for analysis.

[0070]FIGS. 8 and 9 depict flowcharts of example methodologies 800, 900
for analyzing, rating or optimizing an electronic social network
according to additional aspects of the subject disclosure. At 802, method
800 can obtain data descriptive of personal inter-relationships
comprising a social network, as described herein. At 804, method 800 can
identify a set of users of a communication network that are included in
the social network. At 806, method 800 can monitor user-network interface
devices to identify user-related device activities. At 808, method 800
can monitor data generated at, disseminated among or consumed by among
various user-network interface devices to identify or characterize
device-related tasks or communications engaged in by the set of users. At
810, method 800 can identify network or user tasks based on the monitored
data. At 812, method 800 can characterize task performance based at least
in part on the capability of the social network, or applications,
features or functions thereof, to disseminate data or facilitate data
consumption. At 814, method 800 can identify a particular task, aspect or
function of the social network for analysis. At 816, method 800 can
obtain a performance benchmark tuned to the particular
task/aspect/function. At 818, method 800 can rate the social or
communication network compared with the performance benchmark, to provide
a relative measure of effectiveness in implementing the particular task,
aspect or function. In some aspects, rating the social network involves
rating the composition of user nodes or user node interactions utilized
to associate persons based on personal interactions. According to
additional aspects, rating the communication network can comprise rating
social network functions and applications, such as data dissemination,
data storage, analysis of user contextual information and ability to pair
or recommend users based on common interests, expertise, needs, hobbies,
and so on.

[0071]Referring now to FIG. 9 at 902, method 900 can continue from method
800 at 818, and analyze the networks based on the performance rating. The
analysis can comprise referencing a model network composition,
infrastructure, associations or features/applications utilized to train a
performance benchmark. At 904, method 900 can make appropriate
comparisons between the social network under test and the model network.
At 906, method 900 can identify an optimized composition or structure of
the social network under test based on disparity in network performances,
and differences in network composition, and network features and
applications. As an example, identification of data-flow bottlenecks,
whether based on communication network infrastructure or social network
user nodes, can be identified and recommendations for mitigating the
bottleneck (e.g., by changing composition of the social network, or
recommending updates to infrastructure applications or equipment)
provided. At 908, method 900 can output the optimization to a user for
network improvement. In at least one aspect of the subject disclosure,
the output can comprise an output file configured to automatically
implement changes to network composition, or software based on suitable
user-node and connectivity parameters or included applications and code,
respectively, written to the output file.

[0072]Referring now to FIG. 10, there is illustrated a block diagram of an
exemplary computer system operable to execute the disclosed architecture.
In order to provide additional context for various aspects of the claimed
subject matter, FIG. 10 and the following discussion are intended to
provide a brief, general description of a suitable computing environment
1000 in which the various aspects of the claimed subject matter can be
implemented. Additionally, while the claimed subject matter described
above can be suitable for application in the general context of
computer-executable instructions that can run on one or more computers,
those skilled in the art will recognize that the claimed subject matter
also can be implemented in combination with other program modules and/or
as a combination of hardware and software.

[0073]Generally, program modules include routines, programs, components,
data structures, etc., that perform particular tasks or implement
particular abstract data types. Moreover, those skilled in the art will
appreciate that the inventive methods can be practiced with other
computer system configurations, including single-processor or
multiprocessor computer systems, minicomputers, mainframe computers, as
well as personal computers, hand-held computing devices,
microprocessor-based or programmable consumer electronics, and the like,
each of which can be operatively coupled to one or more associated
devices.

[0074]The illustrated aspects of the claimed subject matter can also be
practiced in distributed computing environments where certain tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed computing environment, program
modules can be located in both local and remote memory storage devices.

[0075]A computer typically includes a variety of computer-readable media.
Computer-readable media can be any available media that can be accessed
by the computer and includes both volatile and nonvolatile media,
removable and non-removable media. By way of example, and not limitation,
computer-readable media can comprise computer storage media and
communication media. Computer storage media can include both volatile and
nonvolatile, removable and non-removable media implemented in any method
or technology for storage of information such as computer-readable
instructions, data structures, program modules or other data. Computer
storage media includes, but is not limited to, RAM, ROM, EEPROM, flash
memory or other memory technology, CD-ROM, digital versatile disk (DVD)
or other optical disk storage, magnetic cassettes, magnetic tape,
magnetic disk storage or other magnetic storage devices, or any other
medium which can be used to store the desired information and which can
be accessed by the computer.

[0076]Communication media typically embodies computer-readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport
mechanism, and includes any information delivery media. The term
"modulated data signal" means a signal that has one or more of its
characteristics set or changed in such a manner as to encode information
in the signal. By way of example, and not limitation, communication media
includes wired media such as a wired network or direct-wired connection,
and wireless media such as acoustic, RF, infrared and other wireless
media. Combinations of the any of the above should also be included
within the scope of computer-readable media.

[0077]Continuing to reference FIG. 10, the exemplary environment 1000 for
implementing various aspects of the claimed subject matter includes a
computer 1002, the computer 1002 including a processing unit 1004, a
system memory 1006 and a system bus 1008. The system bus 1008 couples to
system components including, but not limited to, the system memory 1006
to the processing unit 1004. The processing unit 1004 can be any of
various commercially available processors. Dual microprocessors and other
multi-processor architectures can also be employed as the processing unit
1004.

[0078]The system bus 1008 can be any of several types of bus structure
that can further interconnect to a memory bus (with or without a memory
controller), a peripheral bus, and a local bus using any of a variety of
commercially available bus architectures. The system memory 1006 includes
read-only memory (ROM) 1010 and random access memory (RAM) 1012. A basic
input/output system (BIOS) is stored in a non-volatile memory 1010 such
as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help
to transfer information between elements within the computer 1002, such
as during start-up. The RAM 1012 can also include a high-speed RAM such
as static RAM for caching data.

[0079]The computer 1002 further includes an internal hard disk drive (HDD)
1014A (e.g., EIDE, SATA), which internal hard disk drive 1014A can also
be configured for external use (1014B) in a suitable chassis (not shown),
a magnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to
a removable diskette 1018) and an optical disk drive 1020, (e.g., reading
a CD-ROM disk 1022 or, to read from or write to other high capacity
optical media such as the DVD). The hard disk drive 1014, magnetic disk
drive 1016 and optical disk drive 1020 can be connected to the system bus
1008 by a hard disk drive interface 1024, a magnetic disk drive interface
1026 and an optical drive interface 1028, respectively. The interface
1024 for external drive implementations includes at least one or both of
Universal Serial Bus (USB) and IEEE1394 interface technologies. Other
external drive connection technologies are within contemplation of the
subject matter claimed herein.

[0080]The drives and their associated computer-readable media provide
nonvolatile storage of data, data structures, computer-executable
instructions, and so forth. For the computer 1002, the drives and media
accommodate the storage of any data in a suitable digital format.
Although the description of computer-readable media above refers to a
HDD, a removable magnetic diskette, and a removable optical media such as
a CD or DVD, it should be appreciated by those skilled in the art that
other types of media which are readable by a computer, such as zip
drives, magnetic cassettes, flash memory cards, cartridges, and the like,
can also be used in the exemplary operating environment, and further,
that any such media can contain computer-executable instructions for
performing the methods of the claimed subject matter.

[0081]A number of program modules can be stored in the drives and RAM
1012, including an operating system 1030, one or more application
programs 1032, other program modules 1034 and program data 1036. All or
portions of the operating system, applications, modules, and/or data can
also be cached in the RAM 1012. It is appreciated that the claimed
subject matter can be implemented with various commercially available
operating systems or combinations of operating systems.

[0082]A user can enter commands and information into the computer 1002
through one or more wired/wireless input devices, e.g., a keyboard 1038
and a pointing device, such as a mouse 1040. Other input devices (not
shown) can include a microphone, an IR remote control, a joystick, a game
pad, a stylus pen, touch screen, or the like. These and other input
devices are often connected to the processing unit 1004 through an input
device interface 1042 that is coupled to the system bus 1008, but can be
connected by other interfaces, such as a parallel port, an IEEE1394
serial port, a game port, a USB port, an IR interface, etc.

[0083]A monitor 1044 or other type of display device is also connected to
the system bus 1008 via an interface, such as a video adapter 1046. In
addition to the monitor 1044, a computer typically includes other
peripheral output devices (not shown), such as speakers, printers, etc.

[0084]The computer 1002 can operate in a networked environment using
logical connections via wired and/or wireless communications to one or
more remote computers, such as a remote computer(s) 1048. The remote
computer(s) 1048 can be a workstation, a server computer, a router, a
personal computer, portable computer, microprocessor-based entertainment
appliance, a peer device or other common network node, and typically
includes many or all of the elements described relative to the computer
1002, although, for purposes of brevity, only a memory/storage device
1050 is illustrated. The logical connections depicted include
wired/wireless connectivity to a local area network (LAN) 1052 and/or
larger networks, e.g., a wide area network (WAN) 1054. Such LAN and WAN
networking environments are commonplace in offices and companies, and
facilitate enterprise-wide computer networks, such as intranets, all of
which can connect to a global communications network, e.g., the Internet.

[0085]When used in a LAN networking environment, the computer 1002 is
connected to the local network 1052 through a wired and/or wireless
communication network interface or adapter 1056. The adapter 1056 can
facilitate wired or wireless communication to the LAN 1052, which can
also include a wireless access point disposed thereon for communicating
with the wireless adapter 1056.

[0086]When used in a WAN networking environment, the computer 1002 can
include a modem 1058, or is connected to a communications server on the
WAN 1054, or has other means for establishing communications over the WAN
1054, such as by way of the Internet. The modem 1058, which can be
internal or external and a wired or wireless device, is connected to the
system bus 1008 via the serial port interface 1042. In a networked
environment, program modules depicted relative to the computer 1002, or
portions thereof, can be stored in the remote memory/storage device 1050.
It will be appreciated that the network connections shown are exemplary
and other means of establishing a communications link between the
computers can be used.

[0087]The computer 1002 is operable to communicate with any wireless
devices or entities operatively disposed in wireless communication, e.g.,
a printer, scanner, desktop and/or portable computer, portable data
assistant, communications satellite, any piece of equipment or location
associated with a wirelessly detectable tag (e.g., a kiosk, news stand,
restroom), and telephone. This includes at least WiFi and Bluetooth®
wireless technologies. Thus, the communication can be a predefined
structure as with a conventional network or simply an ad hoc
communication between at least two devices.

[0088]WiFi, or Wireless Fidelity, allows connection to the Internet from a
couch at home, a bed in a hotel room, or a conference room at work,
without wires. WiFi is a wireless technology similar to that used in a
cell phone that enables such devices, e.g., computers, to send and
receive data indoors and out; anywhere within the range of a base
station. WiFi networks use radio technologies called IEEE802.11 (a, b, g,
n, etc.) to provide secure, reliable, fast wireless connectivity. A WiFi
network can be used to connect computers to each other, to the Internet,
and to wired networks (which use IEEE802.3 or Ethernet). WiFi networks
operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps
(802.11a) or 54 Mbps (802.11b) data rate, for example, or with products
that contain both bands (dual band), so the networks can provide
real-world performance similar to the basic 10BaseT wired Ethernet
networks used in many offices.

[0089]Referring now to FIG. 11, there is illustrated a schematic block
diagram of an exemplary computer compilation system operable to execute
the disclosed architecture. The system 1100 includes one or more
client(s) 1102. The client(s) 1102 can be hardware and/or software (e.g.,
threads, processes, computing devices). The client(s) 1102 can house
cookie(s) and/or associated contextual information by employing the
claimed subject matter, for example.

[0090]The system 1100 also includes one or more server(s) 1104. The
server(s) 1104 can also be hardware and/or software (e.g., threads,
processes, computing devices). The servers 1104 can house threads to
perform transformations by employing the claimed subject matter, for
example. One possible communication between a client 1102 and a server
1104 can be in the form of a data packet adapted to be transmitted
between two or more computer processes. The data packet can include a
cookie and/or associated contextual information, for example. The system
1100 includes a communication framework 1106 (e.g., a global
communication network such as the Internet) that can be employed to
facilitate communications between the client(s) 1102 and the server(s)
1104.

[0091]Communications can be facilitated via a wired (including optical
fiber) and/or wireless technology. The client(s) 1102 are operatively
connected to one or more client data store(s) 1108 that can be employed
to store information local to the client(s) 1102 (e.g., cookie(s) and/or
associated contextual information). Similarly, the server(s) 1104 are
operatively connected to one or more server data store(s) 1110 that can
be employed to store information local to the servers 1104.

[0092]What has been described above includes examples of the various
embodiments. It is, of course, not possible to describe every conceivable
combination of components or methodologies for purposes of describing the
embodiments, but one of ordinary skill in the art can recognize that many
further combinations and permutations are possible. Accordingly, the
detailed description is intended to embrace all such alterations,
modifications, and variations that fall within the spirit and scope of
the appended claims.

[0093]In particular and in regard to the various functions performed by
the above described components, devices, circuits, systems and the like,
the terms (including a reference to a "means") used to describe such
components are intended to correspond, unless otherwise indicated, to any
component which performs the specified function of the described
component (e.g., a functional equivalent), even though not structurally
equivalent to the disclosed structure, which performs the function in the
herein illustrated exemplary aspects of the embodiments. In this regard,
it will also be recognized that the embodiments include a system as well
as a computer-readable medium having computer-executable instructions for
performing the acts and/or events of the various methods.

[0094]In addition, while a particular feature may have been disclosed with
respect to only one of several implementations, such feature can be
combined with one or more other features of the other implementations as
may be desired and advantageous for any given or particular application.
Furthermore, to the extent that the terms "includes," and "including" and
variants thereof are used in either the detailed description or the
claims, these terms are intended to be inclusive in a manner similar to
the term "comprising."